Advertisement

Rhetorical Map of an Answer

  • Boris Galitsky
Chapter

Abstract

In this Chapter we explore an anatomy of an arbitrary text with respect to how it can answer questions. One more opportunity for discourse analysis to assist with topical relevance of an answer is identified. We discover that a discourse tree of an answer sheds a light on how an answer is constructed, and how to treat keyword occurrence. There is a simple observation employed by search engines: keywords from a query need to occur in a single answer sentence, for this answer to be relevant. Relying on answer anatomy, we substantially extend the notion of how query keywords should occur in answer areas such as its elementary discourse units. We explore how to identify informative and uninformative parts of answers in terms of matching with questions. It turns out that discourse trees contribute a lot in building answer maps which are fairly important for determining whether this answer is good or not for a given question.

References

  1. Asher N, Lascarides A (2003) Logics of conversation. Cambridge University Press, CambridgeGoogle Scholar
  2. Chali Y, Joty SR, Hasan SA (2009) Complex question answering: unsupervised learning approaches and experiments. J Artif Intell Res 35(1):1–47MathSciNetCrossRefGoogle Scholar
  3. Croft B, Metzler D, Strohman T (2009) Search engines – information retrieval in practice. Pearson Education. North AmericaGoogle Scholar
  4. Finn PJ (1975) A question writing algorithm. J Read Behav 7(4):341–367CrossRefGoogle Scholar
  5. Galitsky B (2014) Learning parse structure of paragraphs and its applications in search. Eng Appl Artif Intell 32:160–184CrossRefGoogle Scholar
  6. Galitsky B (2017a) Discovering rhetorical agreement between a request and response. Dialogue Discourse 8(2):167–205Google Scholar
  7. Galitsky B (2017b) Matching parse thickets for open domain question answering. Data Knowl Eng 107:24–50CrossRefGoogle Scholar
  8. Galitsky B, Ilvovsky D (2017a) Chatbot with a discourse structure-driven dialogue management, EACL demo programGoogle Scholar
  9. Galitsky B, Ilvovsky D (2017b) On a chat bot finding answers with optimal rhetoric representation. Proceedings of recent advances in natural language processing, pages 253–259, Varna, Bulgaria, Sept 4–6Google Scholar
  10. Galitsky B, Kovalerchuk B (2014) Improving web search relevance with learning structure of domain concepts. In: Clusters, orders, and trees: methods and applications. Springer, New York, pp 341–376Google Scholar
  11. Galitsky B, Lebedeva N (2015) Recognizing documents versus meta-documents by tree Kernel learning. FLAIRS conference, pp 540–545Google Scholar
  12. Galitsky B, Gabor Dobrocsi J, Lluis de la R (2012) Inferring the semantic properties of sentences by mining syntactic parse trees. Data Knowl Eng 81:21–45CrossRefGoogle Scholar
  13. Galitsky B, Kuznetsov SO, Usikov D (2013) Parse thicket representation for multi-sentence search. International conference on conceptual structures, pp 153–172Google Scholar
  14. Galitsky B, Ilvovsky D, Kuznetsov SO (2015a) Text classification into abstract classes based on discourse structure. Proceedings of recent advances in natural language processing, pages 200–207, Hissar, Bulgaria, Sep 7–9 2015Google Scholar
  15. Galitsky B, Ilvovsky D, Kuznetsov SO (2015b) Rhetoric map of an answer to compound queries. Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing. Volume 2, pp 681–686Google Scholar
  16. Hobbs JR (1979) Coherence and coreference. Cogn Sci 3(1):67–90CrossRefGoogle Scholar
  17. Hobbs JR (1985) On the coherence and structure of discourse. Report no. CSLI-85-37, center for the study of language and information, OctoberGoogle Scholar
  18. Ilvovsky D (2014) Going beyond sentences when applying tree kernels. Proceedings of the ACL 2014 student research workshop, pp 56–63Google Scholar
  19. Jansen P, Surdeanu M, Clark P (2014) Discourse complements lexical semantics for nonfactoid answer reranking. ACLGoogle Scholar
  20. Jasinskaja K, Karagjosova E (2017) Rhetorical relations. In: Matthewson L, Meier C, Rullmann H, Zimmermann TE (eds) The companion to semantics. Wiley, OxfordGoogle Scholar
  21. Joty SR, Moschitti A (2014) Discriminative reranking of discourse parses using tree Kernels. Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)Google Scholar
  22. Joty SR, Carenini G, Ng RT, Mehdad Y (2013) Combining intra-and multi- sentential rhetorical parsing for document-level discourse analysis. In: ACL (1), pages 486–496Google Scholar
  23. Mann W, Thompson S (1988) Rhetorical structure theory: towards a functional theory of text organization. Text-Interdiscip J Study of Discourse 8(3):243–281CrossRefGoogle Scholar
  24. Moschitti A (2006) Efficient convolution Kernels for dependency and constituent syntactic trees. In: Proceedings of the 17th european conference on machine learning, Berlin, GermanyGoogle Scholar
  25. QnAmaker (2018) Microsoft QnA Maker. https://www.qnamaker.ai/
  26. Strok F, Galitsky B, Dmitry Ilvovsky, Kuznetsov SO (2014) Pattern structure projections for learning discourse structures. International conference on artificial intelligence: methodology, systems, and applications, pp 254–260Google Scholar
  27. Surdeanu M, Hicks T, Valenzuela-Escarcega MA (2015) Two practical rhetorical structure theory parsers. Proceedings of the conference of the North American chapter of the association for computational linguistics – human language technologies: software demonstrations (NAACL HLT)Google Scholar
  28. Sweetser E (1990) From etymology to pragmatics: metaphorical and cultural aspects of semantic structure (Cambridge studies in linguistics). Cambridge University Press, CambridgeCrossRefGoogle Scholar
  29. Yahoo! Answers (2018) https://answers.yahoo.com/

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Boris Galitsky
    • 1
  1. 1.Oracle (United States)San JoseUSA

Personalised recommendations